Demonstration of Universal Parametric Entangling Gates on a Multi-Qubit Lattice

We show that parametric coupling techniques can be used to generate selective entangling interactions for multi-qubit processors. By inducing coherent population exchange between adjacent qubits under frequency modulation, we implement a universal gateset for a linear array of four superconducting qubits. An average process fidelity of $\mathcal{F}=93\%$ is estimated for three two-qubit gates via quantum process tomography. We establish the suitability of these techniques for computation by preparing a four-qubit maximally entangled state and comparing the estimated state fidelity against the expected performance of the individual entangling gates. In addition, we prepare an eight-qubit register in all possible bitstring permutations and monitor the fidelity of a two-qubit gate across one pair of these qubits. Across all such permutations, an average fidelity of $\mathcal{F}=91.6\pm2.6\%$ is observed. These results thus offer a path to a scalable architecture with high selectivity and low crosstalk

Towards secure message systems

Message systems, which transfer information from sender to recipient via communication networks, are indispensable to our modern society. The enormous user base of message systems and their critical role in information delivery make it the top priority to secure message systems. This dissertation focuses on securing the two most representative and dominant messages systems---e-mail and instant messaging (IM)---from two complementary aspects: defending against unwanted messages and ensuring reliable delivery of wanted messages.;To curtail unwanted messages and protect e-mail and instant messaging users, this dissertation proposes two mechanisms DBSpam and HoneyIM, which can effectively thwart e-mail spam laundering and foil malicious instant message spreading, respectively. DBSpam exploits the distinct characteristics of connection correlation and packet symmetry embedded in the behavior of spam laundering and utilizes a simple statistical method, Sequential Probability Ratio Test, to detect and break spam laundering activities inside a customer network in a timely manner. The experimental results demonstrate that DBSpam is effective in quickly and accurately capturing and suppressing e-mail spam laundering activities and is capable of coping with high speed network traffic. HoneyIM leverages the inherent characteristic of spreading of IM malware and applies the honey-pot technology to the detection of malicious instant messages. More specifically, HoneyIM uses decoy accounts in normal users\u27 contact lists as honey-pots to capture malicious messages sent by IM malware and suppresses the spread of malicious instant messages by performing network-wide blocking. The efficacy of HoneyIM has been validated through both simulations and real experiments.;To improve e-mail reliability, that is, prevent losses of wanted e-mail, this dissertation proposes a collaboration-based autonomous e-mail reputation system called CARE. CARE introduces inter-domain collaboration without central authority or third party and enables each e-mail service provider to independently build its reputation database, including frequently contacted and unacquainted sending domains, based on the local e-mail history and the information exchanged with other collaborating domains. The effectiveness of CARE on improving e-mail reliability has been validated through a number of experiments, including a comparison of two large e-mail log traces from two universities, a real experiment of DNS snooping on more than 36,000 domains, and extensive simulation experiments in a large-scale environment

Cyber-crime Science = Crime Science + Information Security

Cyber-crime Science is an emerging area of study aiming to prevent cyber-crime by combining security protection techniques from Information Security with empirical research methods used in Crime Science. Information security research has developed techniques for protecting the confidentiality, integrity, and availability of information assets but is less strong on the empirical study of the effectiveness of these techniques. Crime Science studies the effect of crime prevention techniques empirically in the real world, and proposes improvements to these techniques based on this. Combining both approaches, Cyber-crime Science transfers and further develops Information Security techniques to prevent cyber-crime, and empirically studies the effectiveness of these techniques in the real world. In this paper we review the main contributions of Crime Science as of today, illustrate its application to a typical Information Security problem, namely phishing, explore the interdisciplinary structure of Cyber-crime Science, and present an agenda for research in Cyber-crime Science in the form of a set of suggested research questions

Incremental learning of concept drift from imbalanced data

Learning data sampled from a nonstationary distribution has been shown to be a very challenging problem in machine learning, because the joint probability distribution between the data and classes evolve over time. Thus learners must adapt their knowledge base, including their structure or parameters, to remain as strong predictors. This phenomenon of learning from an evolving data source is akin to learning how to play a game while the rules of the game are changed, and it is traditionally referred to as learning concept drift. Climate data, financial data, epidemiological data, spam detection are examples of applications that give rise to concept drift problems. An additional challenge arises when the classes to be learned are not represented (approximately) equally in the training data, as most machine learning algorithms work well only when the class distributions are balanced. However, rare categories are commonly faced in real-world applications, which leads to skewed or imbalanced datasets. Fraud detection, rare disease diagnosis, anomaly detection are examples of applications that feature imbalanced datasets, where data from category are severely underrepresented. Concept drift and class imbalance are traditionally addressed separately in machine learning, yet data streams can experience both phenomena. This work introduces Learn++.NIE (nonstationary & imbalanced environments) and Learn++.CDS (concept drift with SMOTE) as two new members of the Learn++ family of incremental learning algorithms that explicitly and simultaneously address the aforementioned phenomena. The former addresses concept drift and class imbalance through modified bagging-based sampling and replacing a class independent error weighting mechanism - which normally favors majority class - with a set of measures that emphasize good predictive accuracy on all classes. The latter integrates Learn++.NSE, an algorithm for concept drift, with the synthetic sampling method known as SMOTE, to cope with class imbalance. This research also includes a thorough evaluation of Learn++.CDS and Learn++.NIE on several real and synthetic datasets and on several figures of merit, showing that both algorithms are able to learn in some of the most difficult learning environments

Experimental study on CO2-sensitive polyacrylamide as potential in-situ sealing agent for CO2 leakage pathways in geological storage sites

As the world pushes for ‘greener’ technologies and carbon neutrality, efforts have focused on creating novel ways to mitigate humankind’s carbon footprint. Carbon capture and storage (CCS) has become a prevalent technique that has proven to be an effective long-term method to safely relocate excess carbon dioxide (CO2) into subsurface formations. However, CCS is a newer technique which requires constant monitoring due to potential leakage pathways present in CO2 storage sites; therefore, a preventive approach to seal leakage pathways is recommended. This dissertation explores the potential of CO2-sensitive polyacrylamide (CO2-SPAM) as a novel sealing agent for enhanced oil recovery (EOR) and CCS applications. This manuscript explores the strength and weaknesses of various CO2-triggered chemicals and selects the appropriate fit for subsurface in-situ sealing. Relevant literature shows that CO2-SPAM can significantly reduce permeability in porous media. Additionally, organically cross-linked polyacrylamide-based gels, of which CO2-SPAM is one, are thermally stable, resistant to low pH levels, highly injectable, and widely used in various industrial processes. These characteristics make CO2-SPAM a suitable candidate for in-situ sealing. Further studies were performed to comprehend the chemical mechanism, rheological behavior, and injection effects of CO2-SPAM into subsurface formations. Firstly, past literature knowledge and organic chemistry principals were used to develop the complete chemical breakdown of CO2-SPAM gel’s synthesis. Secondly, the effect of salt and polyacrylamide (PAM) concentrations on gelation time, gel strength and viscosity were tested through qualitative (Sydansk gel strength coding system) and quantitative methods (rheometer measurement). The results showed that high salinities increase gelation time and decrease gel strength and viscosity, while high PAM concentrations do the opposite. Lastly, the effects on geomechanical stresses caused by CO2-SPAM injection into the subsurface are also addressed by using the image well method for pore pressure estimation, and frictional faulting theory. The final results determined that the injection of aqueous CO2-SPAM would induce seismicity in normal faulting zones dipping at a large array of angles in the plane of failure. These findings are significant as they determine the potential of induced seismicity in the area of CCS, which in this case was the Raton basin

Costs and benefits of superfast broadband in the UK

This paper was commissioned from LSE Enterprise by Convergys Smart Revenue Solutions to stimulate an open and constructive debate among the main stakeholders about the balance between the costs, the revenues, and the societal benefits of ‘superfast’ broadband. The intent has been to analyse the available facts and to propose wider perspectives on economic and social interactions. The paper has two parts: one concentrates on superfast broadband deployment and the associated economic and social implications (for the UK and its service providers), and the other considers alternative social science approaches to these implications. Both parts consider the potential contribution of smart solutions to superfast broadband provision and use. Whereas Part I takes the “national perspective” and the “service provider perspective”, which deal with the implications of superfast broadband for the UK and for service providers, Part II views matters in other ways, particularly by looking at how to realise values beyond the market economy, such as those inherent in neighbourliness, trust and democrac

Engines of Order

Over the last decades, and in particular since the widespread adoption of the Internet, encounters with algorithmic procedures for ‘information retrieval’ – the activity of getting some piece of information out of a col-lection or repository of some kind – have become everyday experiences for most people in large parts of the world

Regulatory Theory: Foundations and applications

This volume introduces readers to regulatory theory. Aimed at practitioners, postgraduate students and those interested in regulation as a cross-cutting theme in the social sciences, Regulatory Theory includes chapters on the social-psychological foundations of regulation as well as theories of regulation such as responsive regulation, smart regulation and nodal governance. It explores the key themes of compliance, legal pluralism, meta-regulation, the rule of law, risk, accountability, globalisation and regulatory capitalism. The environment, crime, health, human rights, investment, migration and tax are among the fields of regulation considered in this ground-breaking book. Each chapter introduces the reader to key concepts and ideas and contains suggestions for further reading. The contributors, who either are or have been connected to the Regulatory Institutions Network (RegNet) at The Australian National University, include John Braithwaite, Valerie Braithwaite, Peter Grabosky, Neil Gunningham, Fiona Haines, Terry Halliday, David Levi-Faur, Christine Parker, Colin Scott and Clifford Shearing